We describe a new technology for using small collections of example sentences to automatically restrict a speech recognition grammar to allow only the more plausi- ble subset of the sentences it would other- wise admit. This technology is unusual because it bridges the gap between hand-built grammars (used with no training data) and statistical approaches (which require significant data).

impact of language models and loss functions on repair disfluency detection

We have developed a method of improving and controlling the accuracy of automated continuous speech recognition through linguistic postprocessing. In this approach, an output from a speech recognitio n system is passed to a trainable Correction Box module which attempts to locate and repair any transcription errors. The Correction Box consists of a text alignment program, a correction-rule generator, and a series of rule application and verification steps. In the training phase, the correction rules are learned by aligning the recognized speech samples with their original, fully correct versions, on sentence by sentence basis. Misaligned sections give rise to candidate context-free correlation rules, e.g., from ~ frontal; there were made ~ the remainder, etc. Validation against a text corpus leads to context-sensitive correction rules, such as from view ~ frontal view. The system is applied to medical dictation in the area of clinical radiology.